Socio-Technical Optimization and Management of AI-Driven Rural Power Networks for Enhanced Grid Reliability and Agricultural Workforce Productivity

Abstract

The use of Artificial Intelligence (AI) in rural power networks offers a major chance to improve the reliability of the electricity grid while also helping make agricultural work more efficient. This study looks at how to best combine technology and human effort in rural energy systems that use AI. It focuses on how the mix of technology and the way people work in farming areas can be optimized. Based on Socio-Technical Systems Theory and smart grid ideas, the research explores how AI tools like predictive analytics, ways to manage energy demand, and decentralized energy control can make power more stable in rural places. The study uses both numerical data and real-life stories from farmers, technicians, and local leaders in several rural areas. It looks at how AI methods, such as machine learning for predicting energy use and finding problems, work with factors like how hard workers are, when they water crops, and how willing they are to use new tech. The results show that using AI to improve the grid greatly reduces power outages and losses, and also helps farmers by making it easier to plan energy-heavy tasks like watering crops and processing harvests. However, the research also finds some big challenges, such as a lack of knowledge about technology, resistance to using machines, and unequal access to good infrastructure. The study suggests a complete approach that brings together efficient technology, designs that consider human needs, support from policies, and training programs. By connecting reliable power with better productivity for workers, the paper helps with sustainable development in rural areas and gives useful advice for those in government, energy companies, and farming groups who want to use AI to create smarter rural power systems.